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Artificial Intelligence for IoT Cookbook

Artificial Intelligence for IoT Cookbook

By : Roshak
4.9 (10)
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Artificial Intelligence for IoT Cookbook

Artificial Intelligence for IoT Cookbook

4.9 (10)
By: Roshak

Overview of this book

Artificial intelligence (AI) is rapidly finding practical applications across a wide variety of industry verticals, and the Internet of Things (IoT) is one of them. Developers are looking for ways to make IoT devices smarter and to make users’ lives easier. With this AI cookbook, you’ll be able to implement smart analytics using IoT data to gain insights, predict outcomes, and make informed decisions, along with covering advanced AI techniques that facilitate analytics and learning in various IoT applications. Using a recipe-based approach, the book will take you through essential processes such as data collection, data analysis, modeling, statistics and monitoring, and deployment. You’ll use real-life datasets from smart homes, industrial IoT, and smart devices to train and evaluate simple to complex models and make predictions using trained models. Later chapters will take you through the key challenges faced while implementing machine learning, deep learning, and other AI techniques, such as natural language processing (NLP), computer vision, and embedded machine learning for building smart IoT systems. In addition to this, you’ll learn how to deploy models and improve their performance with ease. By the end of this book, you’ll be able to package and deploy end-to-end AI apps and apply best practice solutions to common IoT problems.
Table of Contents (11 chapters)
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How it works...

As always, we import the libraries we need for this project. Next, we import data from our Spark data table into a Pandas DataFrame. One-hot encoding can change categorical values, such as our example of Wine and No Wine, into encoded values that machine learning algorithms can use better. In step 4, we take our feature columns and our one-hot encoded column and perform a split, splitting them into a testing and training set. In step 5, we create a decision tree classifier, use the X_train and y_train data to train the model, and then use the X_test data to create a y_prediction dataset. In other words, in the end, we will have a set of predictions called y_pred based on the predictions the dataset had on the X_test set. In step 6, we evaluate the accuracy of the model and the area under the curve (AUC).

Decision tree classifiers are used when the data is complex. In the same way, you can use a decision tree to follow a set of logical rules using yes/no questions...

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